CN105528719A - Public cloud portal memory price computing method based on multi-level agents - Google Patents
Public cloud portal memory price computing method based on multi-level agents Download PDFInfo
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- CN105528719A CN105528719A CN201510997099.1A CN201510997099A CN105528719A CN 105528719 A CN105528719 A CN 105528719A CN 201510997099 A CN201510997099 A CN 201510997099A CN 105528719 A CN105528719 A CN 105528719A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0278—Product appraisal
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0283—Price estimation or determination
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Abstract
The invention relates to the technical field of cloud computing, in particular to a public cloud portal memory price computing method based on multi-level agents. The method comprises the steps of firstly, configuring, by a superior agent, different levels of memory sale prices via an agent platform; secondly, after a user A applies for an inferior agent (agent A thereinafter), obtaining the memory sale price configured by the superior agent at respective agent platform, namely the memory cost price of the agent A, and setting the memory cost price as P (memory cost); thirdly, configuring, by the agent A, memory price parameter factors (alpha, beta, sigma) of a cloud resource product sold via Internet at a public cloud portal, wherein the three parameter factors respectively correspond to different memory sizes; and finally, automatically computing, by a system, the prices P (memory prices) of different memory sizes of the public cloud portal according to the price parameter factors configured by the agent. The method solves the problems of inflexibility and the like caused when the prices of different memory sizes of the public cloud portal are configured manually, and can be used for computing the memory prices of the public cloud portal.
Description
Technical field
The present invention relates to field of cloud computer technology, particularly a kind of publicly-owned Yunmen based on Multistage Proxy is indoor deposits calculation of price method.
Background technology
Along with the development of cloud computing, charging is the focal issue that publicly-owned cloud user pays close attention to always.User is when cloud product chosen by publicly-owned cloud platform, and price determines one of factor bought often.In practice process, pricing method clearly, rational charging way can reduce the use cost of cloud products customers.
At present, for the charging method that publicly-owned cloud virutal machine memory calculation of price is ununified, be all generally carry out pricing by manually dropping into according to platform itself in conjunction with market intratype competition opponent price.
Take existing method to carry out accounting price, there is following defect:
One is that manual configuration price workload is large, and be especially designed into when acting on behalf of price at many levels, door price needs to consider that the factor level relied on is more;
Two is that arbitrary pricing factor is indefinite, and pricing method is dumb;
Three is do not have clearly special pricing method, only too subjective with artificial empirical value price;
In order to avoid because of human configuration price workload large, publicly-owned Yunmen family price relies on and acts on behalf of price factor complexity at many levels, arbitrary pricing factor is indefinite, mode is dumb, lack computing method of fixing a price specially, with problems such as artificial empirical value configuration rates are too subjective, need one can consider Multistage Proxy operation model, eliminate the clear and definite calculation of price method of the complicated subjectivity price of manual operation.
Summary of the invention
The technical matters that the present invention solves is to provide that a kind of publicly-owned Yunmen based on Multistage Proxy is indoor deposits calculation of price method; Solve because human configuration price workload is large, publicly-owned Yunmen family price relies on and acts on behalf of price factor complexity at many levels, and arbitrary pricing factor is indefinite, and mode is dumb, lacks computing method of fixing a price specially, with problems such as artificial empirical value configuration rates are too subjective.
The technical scheme that the present invention solves the problems of the technologies described above is:
Described method comprises the steps:
Step 1: superior agency is by agent platform configuration different brackets internal memory selling price;
Step 2: user applies to become subordinate agency, obtains agent level and DRAM price corresponding to grade, P (internal memory cost);
Step 3: act on behalf of according to internal memory cost price, configures publicly-owned Yunmen family selling price; Agency to arrange by configuration rates parameter factors (α, β, σ) that publicly-owned Yunmen is indoor deposits resource price;
Step 4: system, automatically according to parameter factors, brings the selling price of the different memory size of formulae discovery into, and computing formula is as memory size N=1, P (internal memory price)=P (internal memory cost) * (1+ α) * N; As memory size N=2, P (internal memory price)=P (internal memory cost) * (1+ β) * N; When memory size N >=4, P (internal memory price)=P (internal memory cost) * (1+ β) * 2+P (internal memory cost) * (1+ σ) * (N-2).
Under described superior agency refers to publicly-owned cloud Multistage Proxy operation mode, the higher level user of current agent.
Described internal memory selling price, for superior agency is to the price of subordinate's agency and sales internal memory.Be the cost price that internal memory is acted on behalf of by subordinate, P (internal memory cost).
The described price parameter factor (α, β, σ), wherein α represents as memory size N=1, brings the parameter factors of formulae discovery into; Wherein β represents as memory size N=2, brings the parameter factors of formulae discovery into; Wherein σ represents as memory size N>=4 (N=2
n, n=nonnegative integer; ) time, bring the parameter factors of formulae discovery into;
Described memory size N represents enumerating of memory size, and its value can write N=2
n, n=nonnegative integer;
Described P (internal memory price) is the DRAM price that publicly-owned cloud platform is sold publicly-owned cloud user, and under normally user wraps year monthly payment purchasing model, N number of GB internal memory uses the price of month.
The invention solves by human configuration publicly-owned Yunmen family different memory size price dumb, and price workload is large, pricing factors is indefinite; Collocation method is complicated, not according to different check figure gradient rapid configuration price, does not have special pricing formula to calculate, and light judges too subjective with artificial estimation; Between different agency level, the reference of price cost free, only to fix a price uncertain rate of profit problem with subjectivity.
Accompanying drawing explanation
Below in conjunction with accompanying drawing, the present invention is further described:
Fig. 1 is the inventive method process flow diagram.
Embodiment
As shown in Figure 1, superior agency is by agent platform configuration different brackets internal memory sales price, and user obtains agent level after applying to become subordinate agency, obtains internal memory cost price, P (internal memory cost); Code is as follows:
According to Fig. 1, subordinate's proxy configurations one group of price parameter factor, α, β, σ, suppose that memory size is N (N=2
n, n=nonnegative integer; α represents when N=1 parameter factors; β represents when N=2 parameter factors; σ represents that, when N>=4, code is as follows:
As shown in Figure 1, according to different internal memory N values, the incorporating parametric factor, bring the corresponding DRAM price of formulae discovery into, code is as follows:
publicdoublegetPrice(Floata，Floatb，Floatc，intmem)
{
doubleprice＝calculate(a，b，c，mem)；
returnprice；
}。
Claims (5)
1. deposit a calculation of price method based on publicly-owned Yunmen of Multistage Proxy is indoor, it is characterized in that: described method comprises the steps:
Step 1: superior agency is by agent platform configuration different brackets internal memory selling price;
Step 2: user applies to become subordinate agency, obtains agent level and DRAM price corresponding to grade, P (internal memory cost);
Step 3: act on behalf of according to internal memory cost price, configures publicly-owned Yunmen family selling price; Agency to arrange by configuration rates parameter factors (α, β, σ) that publicly-owned Yunmen is indoor deposits resource price;
Step 4: system, automatically according to parameter factors, brings the selling price of the different memory size of formulae discovery into, and computing formula is as memory size N=1, P (internal memory price)=P (internal memory cost) * (1+ α) * N; As memory size N=2, P (internal memory price)=P (internal memory cost) * (1+ β) * N; When memory size N >=4, P (internal memory price)=P (internal memory cost) * (1+ β) * 2+P (internal memory cost) * (1+ σ) * (N-2).
2. publicly-owned Yunmen according to claim 1 is indoor deposits calculation of price method, it is characterized in that: under described superior agency refers to publicly-owned cloud Multistage Proxy operation mode, the higher level user of current agent.
3. publicly-owned Yunmen according to claim 1 is indoor deposits calculation of price method, it is characterized in that: described internal memory selling price, for superior agency is to the price of subordinate's agency and sales internal memory.Be the cost price that internal memory is acted on behalf of by subordinate, P (internal memory cost).
4. publicly-owned Yunmen according to claim 2 is indoor deposits calculation of price method, it is characterized in that: described internal memory selling price, for superior agency is to the price of subordinate's agency and sales internal memory.Be the cost price that internal memory is acted on behalf of by subordinate, P (internal memory cost).
5. the publicly-owned Yunmen according to any one of Claims 1-4 is indoor deposits calculation of price method, it is characterized in that: the described price parameter factor (α, β, σ), and wherein α represents as memory size N=1, brings the parameter factors of formulae discovery into; Wherein β represents as memory size N=2, brings the parameter factors of formulae discovery into; Wherein σ represents as memory size N>=4 (N=2
n, n=nonnegative integer; ) time, bring the parameter factors of formulae discovery into;
Described memory size N represents enumerating of memory size, and its value can write N=2
n, n=nonnegative integer;
Described P (internal memory price) is the DRAM price that publicly-owned cloud platform is sold publicly-owned cloud user, and under normally user wraps year monthly payment purchasing model, N number of GB internal memory uses the price of month.
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510997099.1A CN105528719A (en) | 2015-12-24 | 2015-12-24 | Public cloud portal memory price computing method based on multi-level agents |
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| CN201510997099.1A CN105528719A (en) | 2015-12-24 | 2015-12-24 | Public cloud portal memory price computing method based on multi-level agents |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106330927A (en) * | 2016-08-29 | 2017-01-11 | 国云科技股份有限公司 | Multi-level software-defined data centre architecture and method thereof |
| CN106600291A (en) * | 2016-12-07 | 2017-04-26 | 国云科技股份有限公司 | Multi-level agent-based public cloud order delivery method |
-
2015
- 2015-12-24 CN CN201510997099.1A patent/CN105528719A/en active Pending
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106330927A (en) * | 2016-08-29 | 2017-01-11 | 国云科技股份有限公司 | Multi-level software-defined data centre architecture and method thereof |
| CN106330927B (en) * | 2016-08-29 | 2019-10-11 | 国云科技股份有限公司 | A kind of multistage software definition data center architecture and its method |
| CN106600291A (en) * | 2016-12-07 | 2017-04-26 | 国云科技股份有限公司 | Multi-level agent-based public cloud order delivery method |
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Application publication date: 20160427 |
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